As is well known in the art, the use of wireless communication devices such as telephones, pagers, personal digital assistants, laptop computers, anti-theft devices, etc., hereinafter referred to collectively as “mobile devices”, has become prevalent in today's society. Along with the proliferation of these mobile devices is the safety concern associated with the need to locate the mobile device, for example in an emergency situation. For example, the Federal Communication Commission (“FCC”) has issued a geolocation mandate for providers of wireless telephone communication services that puts in place a schedule and an accuracy standard under which the providers of wireless communications must implement geolocation technology for wireless telephones when used to make a 911 emergency telephone call (FCC 94-102 E911). In addition to E911 emergency related issues, there has been increased interest in technology which can determine the geographic position, or “geolocate” a mobile device. For example, wireless telecommunications providers are developing location-enabled services for their subscribers including roadside assistance, turn-by-turn driving directions, concierge services, location-specific billing rates and location-specific advertising.
Currently in the art, there are a number of different ways to geolocate a mobile device. For example, providers of wireless communication services have installed mobile device location capabilities into their networks. In operation, these network overlay location systems take measurements on radio frequency (“RF”) transmissions from mobile devices at base station locations surrounding the mobile device and estimate the location of the mobile device with respect to the base stations or other radio transmitter. Because the geographic location of the base stations is known, the determination of the location of the mobile device with respect to the base station permits the geographic location of the mobile device to be determined. The RF measurements of the transmitted signal can include the time of arrival, the angle of arrival, the signal power, or the unique/repeatable radio propagation path (radio fingerprinting) derivable features. In addition, the geolocation systems can also use collateral information, e.g., information other than that derived for the RF measurement to assist in the geolocation of the mobile device, i.e., location of roads, dead-reckoning, topography, map matching, etc.
In a network-based geolocation system, the mobile device to be located is typically identified and radio channel assignments determined by (a) monitoring the control information transmitted on radio channel for telephone calls being placed by the mobile device or on a wireline interface to detect calls of interest, i.e., 911, (b) a location request provided by a non-mobile device source, i.e., an enhanced services provider. Once a mobile device to be located has been identified and radio channel assignments determined, the location determining system is tasked to determine the geolocation of the mobile device and then directed to report the determined position to the requesting entity or enhanced services provider.
Location determination in a wireless network relies on measurement data that is acquired from a number of sources, including radio transmitters and target devices (mobile phones, PDAs, etc). The accuracy of the location determination is not only based on the accuracy of the measurement data, but also the network topology (i.e. the geometry of the base stations and the mobile device).
Some location determination techniques rely on a statistical analysis of measurement data. Multiple measurements are taken and a probability distribution is formed. Providing high quality results can require that multiple measurements of the same value are taken. While an individual measurement might be subject to a high degree of uncertainty, the quality of the result can be improved by increasing the number of measurements.
Measurements that are highly subject to variation are most often subject to such a treatment. For instance, radio signal strength is rarely constant due to atmospheric effects and transmitter fluctuations. Similarly, timing measurements can be subject to variable delay through atmosphere and to varying multipath effects.
The extent to which multiple measurements are necessary varies greatly. For trilateration methods such as time difference of arrival (TDOA) where distances from fixed transmitters are measured, the accuracy required of each measurement is largely based on the geometry of the transmitter in relation to the position of the target device.
Dilution of precision (DOP) is a measure of how the geometry of transmitters affects the potential result. From any given point, DOP values—in three dimensions (geometry dilution of precision, or GDOP) and in two dimensions (horizontal dilution of precision, or HDOP)—can be used to indicate the effect that poor quality measurement data has on the final result.
The computation of DOP involves the geometry from the receiver (or point) to the transmitter, which may be represented as unit vectors from the receiver to radio transmitter i.
            (                        x          i                -        x            )              R      i        ,            (                        y          i                -        y            )              R      i        ,      and    ⁢                  ⁢                  (                              z            i                    -          z                )                    R        i            where
Ri=√{square root over ((xi−x)2+(yi−y)2+(zi−z)2)}{square root over ((xi−x)2+(yi−y)2+(zi−z)2)}{square root over ((xi−x)2+(yi−y)2+(zi−z)2)} and where x, y, and z denote the position of the receiver (selected point) and xi, yi, and zi denote the question of radio transmitter i. The matrix A may be formulated as:
  A  =      [                                                      (                                                x                  1                                -                x                            )                                      R              1                                                                          (                                                y                  1                                -                y                            )                                      R              1                                                                          (                                                z                  1                                -                z                            )                                      R              1                                                c                                                                (                                                x                  2                                -                x                            )                                      R              2                                                                          (                                                y                  2                                -                y                            )                                      R              2                                                                          (                                                z                  2                                -                z                            )                                      R              2                                                c                                                                (                                                x                  3                                -                x                            )                                      R              3                                                                          (                                                y                  3                                -                y                            )                                      R              3                                                                          (                                                z                  3                                -                z                            )                                      R              3                                                c                                                                (                                                x                  4                                -                x                            )                                      R              4                                                                          (                                                y                  4                                -                y                            )                                      R              4                                                                          (                                                z                  4                                -                z                            )                                      R              4                                                c                      ]  
The first three elements of each row of A are the components of a unit vector from the receiver to the indicated radio transmitter. The elements in the fourth column are c where c denotes the speed of light. The matrix, Q, may be formulated asQ=(ATA)−1 
where the weighting matrix, P, has been set to the identity matrix.
The elements of the Q matrix are designated as
  Q  =      [                                        d            x            2                                                d            xy            2                                                d            xz            2                                                d            xt            2                                                            d            xy            2                                                d            y            2                                                d            yz            2                                                d            yt            2                                                            d            xz            2                                                d            yz            2                                                d            z            2                                                d            zt            2                                                            d            xt            2                                                d            yt            2                                                d            zt            2                                                d            t            2                                ]  
PDOP, TDOP and GDOP may be given by:PDOP=√{square root over (dx2+dy2+dz2)},TDOP=√{square root over (dt2)}, andGDOP=√{square root over (PDOP2+TDOP2)}
The horizontal dilution of precision, HDOP=√{square root over (dx2+dy2)}, and the vertical dilution of precision, VDOP=√{square root over (dz2)}, are both dependent on the coordinate system used. To correspond to the local horizon plane and the local vertical, x, y, and z should denote positions in either a North, East, Down coordinate system or a South, East, Up coordinate system.
FIG. 1 illustrates a network topology in which a Comparison of good and bad DOP (HDOP) is demonstrated. The radio transmitters 102a-f are located across the network at known location. A mobile device, such as mobile devices 104a or b may determine their location based on signals received from the radio transmitters 102. Mobile device 104a has a relatively good horizontal dilution of precision in that the vectors between it and each of the radio transmitters are diverse, or more orthogonal to each other. Whereas the DOP of mobile device 104b has a relatively bad HDOP as the vectors to each of the radio transmitters 102 are more similar.
Many trilateration systems use the following simple relationship between horizontal and vertical uncertainty with the corresponding DOP value and the measurement error:Horizontal Uncertainty (@95% confidence)=2×HDOP×MESD (Measurement error standard deviation)Vertical Uncertainty (@95% confidence)=2×VDOP×MESD
The measurement error standard deviation (MESD) indicates the degree to which further measurements or calculation show the same or similar results. Therefore, to achieve a particular target uncertainty, measurement error must decrease as DOP increases. The level of uncertainty has an inverse relationship with the confidence level. A high level of uncertainty means a low confidence level and a low level of uncertainty has a high level of confidence. The level of uncertainty may be measured by confidence level. One of the best means of decreasing measurement error is to take more measurements.
The problem is determining how many measurements are needed to produce a result with acceptable quality without excessive cost. Obviously, acquiring multiple measurements takes time and taking measurements also expends limited battery resources in mobile devices. Other operational impacts, such as temporarily silencing radio transmitters can have more wide-ranging impact on the network and its operations as well other network users.
In addition, many networks place the element responsible for location determination at a central location, far remote from the elements that provide measurements. Usually, this is determined by scale: a single high-capacity location server is capable of serving a very large geographic area. However, the distances involved lead to a fixed overhead in measurement acquisition. Network architectures with multiple intermediaries compound the problem. For example, the location determination element in a WiMAX network is far removed from radio transmitters; the large number of intermediaries between the location determination element and the measuring entities introduces significant network delays in communication.
Iterative location determination relies on single measurements, where the measurement process is repeated until the location determination element is able to produce a result of sufficient quality, such as a desired level of uncertainty. However, this method incurs the full cost of the network and measurement overhead for each measurement: initiating a measurement session, network transit delays, and measurement setup (for instance, some radio transmitters might be momentarily disabled to reduce the noise they produce from obscuring measurement of another transmitter).
Determining the optimum number of measurements to acquire ahead of time can provide a significant benefit in efficiently producing a location estimate of the desired quality and in a timely fashion.
The present subject matter in order to obviate the deficiencies of the prior art discloses a novel method for estimating dilution of precision (DOP) across a service area. An embodiment of the method includes selecting points in an area; determining radio transmitters that are capable of being received in the area, calculating a DOP for each the points based on network topology and modeling a DOP function of the service area as a function of the calculated DOP for each of the plurality of points.
The present subject matter also presents a novel method of optimizing the number of measurements requested in an area. An embodiment of the method selects a level of uncertainty; determines a set of radio transmitters capable of being received in the area; and, determines a metric across the area based at least on the geometry of the radio transmitters with respect to randomly distributed points across the area. The method also determines the number of measurements required at a location within the area based on the metric and the selected level of uncertainty.
The present subject matter further presents a novel method for determining the location of a mobile device. An embodiment of the method estimates a first location; retrieves a predetermined network topology metric; and selects a measurement metric using the predetermined topology metric and a predetermined location uncertainty. The embodiment of the method makes first and additional measurements of the property. The additional measurements are predicated on the measurement metric. The embodiment determines the location for the mobile device based at least upon the first measurement and the additional measurements of the property.
The present subject matter additional presents a novel method of adaptively optimizing the amount of measurements of a property required to achieve a desired level of uncertainty in a service area. An embodiment of the method establishes an initial amount (n) of measurements required of a property; when a location request is received, n measurements of the property are obtained; and the location based on the n property measurements is estimated. The embodied method determines the level of uncertainty associated with the estimated location; compares it with at least one threshold which is based on the desired level of uncertainty; and tunes the amount of measurements n based upon the comparison.
The present subject matter presents yet another novel method to locate a mobile device within a predetermined level of uncertainty. An embodiment of the method includes compiling data associated with location requests and location estimates within a service area in a database; associating in the database the amount of plural measurements of a property with the level of uncertainty of a location estimated using the plural measurements. Upon receiving a location request, the method includes determining the level of uncertainty and selecting the amount of plural measurements associated with the level of uncertainty required based on the associations in the database. The embodiment of the method also includes obtaining plural measurements of a property in the amount selected; obtaining a location estimate; determining the level of uncertainty in the location estimate; associating the number of plural measurements obtained and the determined level of uncertainty in the location estimate and compiling the association in the database; and, determining a new amount of measurements required for a desired level of uncertainty.
The present subject matter in addition presents, a novel method of determining a nominal amount of plural measurement of a property required to obtain a predetermined level of uncertainty using actual location requests in a service area subject to property fluctuations. An embodiment of the method includes receiving a location request, estimating a location based on an predetermined level of uncertainty and an amount of property measurements used; associating the amount of measurement samples of a property taken, the estimated location and the resulting level of uncertainty calculated from the estimated location using the measurement samples; and adaptively tuning the associated nominal amount of property measurements based on the estimated location and its level of uncertainty.
The present subject matter presents yet an addition novel method of optimizing the nominal requirements associated with a wireless communications property. An embodiment of the method includes associating location estimates, number of property samples used in the location estimates and level of uncertainties of the location estimates in a historical database; and using the location database to establish nominal requirements. In one embodiment, upon receiving a location request, the method includes generating a location estimate according to the nominal requirements; and then adaptively updating the nominal requirements based on the location estimate.
These and many other objects and advantages of the present invention will be readily apparent to one skilled in the art to which the invention pertains from a perusal or the claims, the appended drawings, and the following detailed description of the preferred embodiments.